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Job Description & How to Apply Below
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Go beyond reporting to deliver actionable recommendations on merchandising strategies, inventory optimization, pricing decisions, promotional effectiveness, and store performance improvements through advanced statistical analysis and predictive modeling.
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Develop and maintain automated Power BI dashboards and data pipelines (Python, SQL) integrating multiple systems (Snowflake, Sage, Krunch), while proactively identifying data quality issues and proposing infrastructure improvements to enhance analytical capabilities.
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Conduct deep-dive analyses including market basket analysis, lost opportunity assessment, demand forecasting, markdown optimization, competitor pricing strategies, and sales driver identification to support C-level decision-making and drive revenue growth.
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Partner with buying, merchandising, marketing, store operations, and finance teams to translate business questions into analytical frameworks, present compelling data stories to senior leadership, and influence strategic initiatives with data-driven recommendations.
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Advanced proficiency in Python (pandas, statistical libraries), SQL, Power BI, Excel, Power Point; strong understanding of retail KPIs and business drivers; ability to apply statistical methods (regression, clustering, ANOVA) and develop forecasting models; experience automating workflows and managing complex multi-source data environments.
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